This review aims at assessing the opportunities and challenges of creating and using digital twins for process industrial systems over their life-cycle in the context of estimation and control. The scope is, therefore, to provide a survey on mechanisms to generate models for process industrial systems using machine learning (purely data-driven) and automated equation-based modeling. In particular, we consider learning, validation, and updating of large-scale (i.e., plant-wide or plant-stage but not component-wide) equation-based process models. These aspects are discussed in relation to typical application cases for the digital twins creating value for users both on the operational and planning level for process industrial systems. These application cases are also connected to the needed technologies and the maturity of those as given by the state of the art. Combining all aspects, a way forward to enable the automatic generation and updating of digital twins is proposed, outlining the required research and development activities. The paper is the outcome of the research project AutoTwin-PRE funded by Strategic Innovation Program PiiA within the Swedish Innovation Agency VINNOVA and the academic version of an industry report prior published by PiiA.

This paper addresses eco-driving of an electric vehicle driving in a hilly terrain under stochastic wind speed uncertainty. The eco-driving problem has been formulated as an optimisation problem, subject to road and traffic information. To enhance the computational efficiency, the dimension of the formulated problem has been reduced by appending trip time dynamics to the problem objective, which is facilitated by necessary Pontryagin's Maximum Principle conditions. To cope with the wind speed uncertainty, stochastic dynamic programming has been applied to solve the problem. Moreover, soft constraints on speed limits (kinetic energy) have been considered in the problem by enforcing sharp penalties in the objective. To benchmark the results, a deterministic controller has also been obtained with the aim of investigating possible constraints violations due to the wind speed uncertainty. For the proposed stochastic controller the optimised speed trajectories always remain within the limits and the violation on the trip time limit is only 8%. On the other hand, the speed and trip time constraints violations for the deterministic controller are 21% and 25%, respectively.